Dataset Information

Automated Detection and Segmentation of Bone Metastases on Spine MRI Using U-Net: A Multicenter Study.

ABSTRACT:

Objective

To develop and evaluate a deep learning model for automated segmentation and detection of bone metastasis on spinal MRI.

Materials and methods

We included whole spine MRI scans of adult patients with bone metastasis: 662 MRI series from 302 patients (63.5 ± 11.5 years; male:female, 151:151) from three study centers obtained between January 2015 and August 2021 for training and internal testing (random split into 536 and 126 series, respectively) and 49 MRI series from 20 patients (65.9 ± 11.5 years; male:female, 11:9) from another center obtained between January 2018 and August 2020 for external testing. Three sagittal MRI sequences, including non-contrast T1-weighted image (T1), contrast-enhanced T1-weighted Dixon fat-only image (FO), and contrast-enhanced fat-suppressed T1-weighted image (CE), were used. Seven models trained using the 2D and 3D U-Nets were developed with different combinations (T1, FO, CE, T1 + FO, T1 + CE, FO + CE, and T1 + FO + CE). The segmentation performance was evaluated using Dice coefficient, pixel-wise recall, and pixel-wise precision. The detection performance was analyzed using per-lesion sensitivity and a free-response receiver operating characteristic curve. The performance of the model was compared with that of five radiologists using the external test set.

Results

The 2D U-Net T1 + CE model exhibited superior segmentation performance in the external test compared to the other models, with a Dice coefficient of 0.699 and pixel-wise recall of 0.653. The T1 + CE model achieved per-lesion sensitivities of 0.828 (497/600) and 0.857 (150/175) for metastases in the internal and external tests, respectively. The radiologists demonstrated a mean per-lesion sensitivity of 0.746 and a mean per-lesion positive predictive value of 0.701 in the external test.

Conclusion

The deep learning models proposed for automated segmentation and detection of bone metastases on spinal MRI demonstrated high diagnostic performance.

SUBMITTER: Kim DH

PROVIDER: S-EPMC10973735 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature

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Publications

Automated Detection and Segmentation of Bone Metastases on Spine MRI Using U-Net: A Multicenter Study.

Kim Dong Hyun DH Seo Jiwoon J Lee Ji Hyun JH Jeon Eun-Tae ET Jeong DongYoung D Chae Hee Dong HD Lee Eugene E Kang Ji Hee JH Choi Yoon-Hee YH Kim Hyo Jin HJ Chai Jee Won JW

Korean journal of radiology 20240401 4

<h4>Objective</h4>To develop and evaluate a deep learning model for automated segmentation and detection of bone metastasis on spinal MRI.<h4>Materials and methods</h4>We included whole spine MRI scans of adult patients with bone metastasis: 662 MRI series from 302 patients (63.5 ± 11.5 years; male:female, 151:151) from three study centers obtained between January 2015 and August 2021 for training and internal testing (random split into 536 and 126 series, respectively) and 49 MRI series from 20 ...[more]

PMID: 38528694

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Dataset Information

Automated Detection and Segmentation of Bone Metastases on Spine MRI Using U-Net: A Multicenter Study.

Objective

Materials and methods

Results

Conclusion

Publications

Automated Detection and Segmentation of Bone Metastases on Spine MRI Using U-Net: A Multicenter Study.

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